SoroushVahidi/scidocs huggingface dataset:
A highly area-efficient controller for capacitive touch screen panel systems
(2010)

In this paper, a highly area-efficient controller for capacitive touch screen panels (TSPs) is proposed. The proposed controller uses a 10-bit successive approximation register analog-to-digital converter (SAR ADC) with an adder to compensate for the capacitance variation in the TSP and for the offset voltage variation in the charge amplifier of the sensing circuit. By using the proposed compensation method, the area of the controller can be reduced by 90.3% of the area of the conventional controllers. The measurement results showed that the signal-to-noise ratio (SNR) of the controller increases from 12.5 to 21.3 dB after compensation. Also, its spatial jitter decreases from ±1.5 to ±0.46 mm, which is 7% of the sensor pitch of 8 mm.
Could Data from Location-Based Social Networks Be Used to Support Urban Planning?
(2017)

A great quantity of information is required to support urban planning. Usually there are many (not integrated) data sources, originating from different government bodies, in distinct formats and variable properties (e.g. reliability, completeness). The effort to handle these data, integrate and analyze them is high, taking to much time for the information to be available to help decision making. We argue that data from location-based social networks (LBSN) could be used to provide useful information in reasonable time, despite several limitations they have. To asses this, as a case study, we used data from different LBSN to calculate the Local Availability Index (IOL) for a Brazilian city. This index is part of a methodology to estimate quality of urban life inside cities and is used to support urban planning. The results suggest that data from LBSN are useful and could be used to provide insights for local governments.
IO Performance Interference among Consolidated n-Tier Applications: Sharing Is Better Than Isolation for Disks
(2014)

The performance unpredictability associated with migrating applications into cloud computing infrastructures has impeded this migration. For example, CPU contention between co-located applications has been shown to exhibit counter-intuitive behavior. In this paper, we investigate IO performance interference through the experimental study of consolidated n-tier applications leveraging the same disk. Surprisingly, we found that specifying a specific disk allocation, e.g., limiting the number of Input/Output Operations Per Second (IOPs) per VM, results in significantly lower performance than fully sharing disk across VMs. Moreover, we observe severe performance interference among VMs can not be totally eliminated even with a sharing strategy (e.g., response times for constant workloads still increase over 1,100%). By using a micro-benchmark (Filebench) and an n-tier application benchmark systems (RUBBoS), we demonstrate the existence of disk contention in consolidated environments, and how performance loss occurs when co-located database systems in order to maintain database consistency flush their logs from memory to disk. Potential solutions to these isolation issues are (1) to increase the log buffer size to amortize the disk IO cost (2) to decrease the number of write threads to alleviate disk contention. We validate these methods experimentally and find a 64% and 57% reduction in response time (or more generally, a reduction in performance interference) for constant and increasing workloads respectively.
Performance of MIMO spatial multiplexing algorithms using indoor channel measurements and models
(2004)

Several algorithms have recently been proposed for multiplexing multiple users in multiple input multiple output (MIMO) downlinks. The ability of a transmitter to accomplish this using spatial methods is generally dependent on whether the users’ channels are correlated. Up to this point, most of the multiplexing algorithms have been tested on uncorrelated Gaussian channels, a best-case scenario. In this paper, we examine the performance of multiplexing multiple users under more realistic channel conditions by using indoor channel measurements and a statistical model for test cases. We use a block zero-forcing algorithm to test performance at various user separation distances, optimizing for both maximum throughput under a power constraint and minimum power under a rate constraint. The results show that for the measured indoor environment (rich scattering, non-line-of-sight), a separation of five wavelengths is enough to achieve close to the maximum available performance for two users. Since many spatial multiplexing algorithms require channel state information (CSI) at the transmitter, we also examine the performance loss due to CSI error. The results show that a user can move up to one-half wavelength before the original channel measurement becomes unusable. Copyright # 2004 John Wiley & Sons, Ltd.
Shilling attack detection utilizing semi-supervised learning method for collaborative recommender system
(2012)

Collaborative filtering (CF) technique is capable of generating personalized recommendations. However, the recommender systems utilizing CF as their key algorithms are vulnerable to shilling attacks which insert malicious user profiles into the systems to push or nuke the reputations of targeted items. There are only a small number of labeled users in most of the practical recommender systems, while a large number of users are unlabeled because it is expensive to obtain their identities. In this paper, Semi-SAD, a new semi-supervised learning based shilling attack detection algorithm is proposed to take advantage of both types of data. It first trains a naïve Bayes classifier on a small set of labeled users, and then incorporates unlabeled users with EM-λ to improve the initial naïve Bayes classifier. Experiments on MovieLens datasets are implemented to compare the efficiency of Semi-SAD with supervised learning based detector and unsupervised learning based detector. The results indicate that Semi-SAD can better detect various kinds of shilling attacks than others, especially against obfuscated and hybrid shilling attacks.
A SMART Stochastic Algorithm for Nonconvex Optimization with Applications to Robust Machine Learning
(2016)

In this paper, we show how to transform any optimization problem that arises from fitting a machine learning model into one that (1) detects and removes contaminated data from the training set while (2) simultaneously fitting the trimmed model on the uncontaminated data that remains. To solve the resulting nonconvex optimization problem, we introduce a fast stochastic proximal-gradient algorithm that incorporates prior knowledge through nonsmooth regularization. For datasets of size n, our approach requires O(n/ε) gradient evaluations to reach ε-accuracy and, when a certain error bound holds, the complexity improves to O(κn log(1/ε)). These rates are n times better than those achieved by typical, full gradient methods.
It’s Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation
(2017)

Eye gaze is an important non-verbal cue for human affect analysis. Recent gaze estimation work indicated that information from the full face region can benefit performance. Pushing this idea further, we propose an appearance-based method that, in contrast to a long-standing line of work in computer vision, only takes the full face image as input. Our method encodes the face image using a convolutional neural network with spatial weights applied on the feature maps to flexibly suppress or enhance information in different facial regions. Through extensive evaluation, we show that our full-face method significantly outperforms the state of the art for both 2D and 3D gaze estimation, achieving improvements of up to 14.3% on MPIIGaze and 27.7% on EYEDIAP for person-independent 3D gaze estimation. We further show that this improvement is consistent across different illumination conditions and gaze directions and particularly pronounced for the most challenging extreme head poses.
Zero Shot Recognition with Unreliable Attributes
(2014)

In principle, zero-shot learning makes it possible to train a recognition model simply by specifying the category’s attributes. For example, with classifiers for generic attributes like striped and four-legged, one can construct a classifier for the zebra category by enumerating which properties it possesses—even without providing zebra training images. In practice, however, the standard zero-shot paradigm suffers because attribute predictions in novel images are hard to get right. We propose a novel random forest approach to train zero-shot models that explicitly accounts for the unreliability of attribute predictions. By leveraging statistics about each attribute’s error tendencies, our method obtains more robust discriminative models for the unseen classes. We further devise extensions to handle the few-shot scenario and unreliable attribute descriptions. On three datasets, we demonstrate the benefit for visual category learning with zero or few training examples, a critical domain for rare categories or categories defined on the fly.
Switchboard: a matchmaking system for multiplayer mobile games
(2011)

Supporting interactive, multiplayer games on mobile phones over cellular networks is a difficult problem. It is particularly relevant now with the explosion of mostly single-player or turn-based games on mobile phones. The challenges stem from the highly variable performance of cellular networks and the need for scalability (not burdening the cellular infrastructure, nor any server resources that a game developer deploys). We have built a service for matchmaking in mobile games -- assigning players to games such that game settings are satisfied as well as latency requirements for an enjoyable game. This requires solving two problems. First, the service needs to know the cellular network latency between game players. Second, the service needs to quickly group players into viable game sessions. In this paper, we present the design of our service, results from our experiments on predicting cellular latency, and results from efficiently grouping players into games.
Bistatic Synthetic Aperture Radar Imaging for Arbitrary Flight Trajectories
(2008)

In this paper, we present an analytic, filtered backprojection (FBP) type inversion method for bistatic synthetic aperture radar (BISAR). We consider a BISAR system where a scene of interest is illuminated by electromagnetic waves that are transmitted, at known times, from positions along an arbitrary, but known, flight trajectory and the scattered waves are measured from positions along a different flight trajectory which is also arbitrary, but known. We assume a single-scattering model for the radar data, and we assume that the ground topography is known but not necessarily flat. We use microlocal analysis to develop the FBP-type reconstruction method. We analyze the computational complexity of the numerical implementation of the method and present numerical simulations to demonstrate its performance.
Modelling of IoT Traffic and Its Impact on LoRaWAN
(2017)

The recent growth of IoT use-cases in a wide array of industrial, utility and environmental applications has necessitated the need for connectivity solutions with diverse requirements. Connectivity through BLE, Zigbee and 6LoPAN are examples of short-range IoT deployments. But to provide connectivity to a high density of devices over larger coverage areas, Low-Power Wide-Area Network (LPWAN) technologies in both licensed as well as unlicensed bands have been considered. In this paper, we consider modelling the traffic from IoT devices connected through LPWAN technologies. Due to diverse applications of IoT, it is not trivial to have a single traffic model to represent all of them, but the traffic can be broadly classified as either periodic, event-triggered, or a combination of both. We evaluate the performance of LoRaWAN, one such LPWAN technology, in the presence of a hybrid of both traffic types, where the event propagates spatially over time. In a practical deployment of sensor based IoT devices, the devices are usually densely deployed to ensure sufficient & reliable measurement. Thereby, when an event occurs, they exhibit spatial & temporal correlation in their traffic rate due to the natural phenomena of the metric they measure. We use the CMMPP model to represent such characteristic traffic from independent IoT devices triggered by an event. The characteristics of LoRa, the physical layer of LoRaWAN, is abstracted based on required signal strength and interference thresholds for different modulation parameters. Through system simulations, we demonstrate that there is a significant performance hit in LoRaWAN based networks, during the occurrence of events. In particular, using the packet delivery rate (PDR) as the metric, we found that while the system was able to handle regular updates from the devices with a PDR > 80%, event-driven traffic nearly impaired the network causing the PDR to drop below 10%.
Multi-Level Sequence GAN for Group Activity Recognition
(2018)

We propose a novel semi supervised, Multi Level Sequential Generative Adversarial Network (MLS-GAN ) architecture for group activity recognition. In contrast to previous works which utilise manually annotated individual human action predictions, we allow the models to learn it’s own internal representations to discover pertinent subactivities that aid the final group activity recognition task. The generator is fed with person-level and scene-level features that are mapped temporally through LSTM networks. Action-based feature fusion is performed through novel gated fusion units that are able to consider long-term dependancies, exploring the relationships among all individual actions, to learn an intermediate representation or ‘action code’ for the current group activity. The network achieves it’s semi-supervised behaviour by allowing it to perform group action classification together with the adversarial real/fake validation. We perform extensive evaluations on different architectural variants to demonstrate the importance of the proposed architecture. Furthermore, we show that utilising both person-level and scene-level features facilitates the group activity prediction better than using only person-level features. Our proposed architecture outperforms current state-of-the-art results for sports and pedestrian based classification tasks on Volleyball and Collective Activity datasets, showing it’s flexible nature for effective learning of group activities. 1
Analyzing User Modeling on Twitter for Personalized News Recommendations
(2011)

How can micro-blogging activities on Twitter be leveraged for user modeling and personalization? In this paper we investigate this question and introduce a framework for user modeling on Twitter which enriches the semantics of Twitter messages (tweets) and identifies topics and entities (e.g. persons, events, products) mentioned in tweets. We analyze how strategies for constructing hashtag-based, entity-based or topic-based user profiles benefit from semantic enrichment and explore the temporal dynamics of those profiles. We further measure and compare the performance of the user modeling strategies in context of a personalized news recommendation system. Our results reveal how semantic enrichment enhances the variety and quality of the generated user profiles. Further, we see how the different user modeling strategies impact personalization and discover that the consideration of temporal profile patterns can improve recommendation quality.
Project massive: self-regulation and problematic use of online gaming
(2007)

A longitudinal design was employed to collect three waves of survey data over a 14 month period from 2790 online gamers. Respondents were asked questions about their gaming activity, motivations, personality, social and emotional environment, and the effect gaming has had on their lives. Prospective analysis was used to establish causal and temporal linkages among the repeatedly measured factors. While the data provide some indication that a player's reasons for playing do influence the development of problematic usage, these effects are overshadowed by the central importance of self-regulation in managing both the timing and amount of play. An individual's level of self-regulatory activity is shown to be very important in allowing them to avoid negative outcomes like problematic use. The role of depression is also discussed. With responsible use, online gaming appears to be a healthy recreational activity that provides millions of people with hours of social entertainment and adaptive diversion. However, failure to manage play behavior can lead to feelings of dependency.
Identifying Users With Alternate Behaviors of Lurking and Active Participation in Multilayer Social Networks
(2018)

With the growing complexity of scenarios relating to online social networks (OSNs), there is an emergence of effective models and methods for understanding the characteristics and dynamics of multiple interconnected types of user relations. Profiles on different OSNs belonging to the same user can be linked using the multilayer structure, opening to unprecedented opportunities for user behavior analysis in a complex system. In this paper, we leverage the importance of studying the dichotomy between information-producers (contributors) and information-consumers (lurkers), and their interplay over a multilayer network, in order to effectively analyze such different roles a user may take on different OSNs. In this respect, we address the novel problem of identification and characterization of opposite behaviors that users may alternately exhibit over multiple layers of a complex network. We propose the first ranking method for alternate lurker-contributor behaviors on a multilayer OSN, dubbed mlALCR. Performance of mlALCR has been assessed quantitatively as well as qualitatively, and comparatively against methods designed for ranking either contributors or lurkers, on four real-world multilayer networks. Empirical evidence shows the significance and uniqueness of mlALCR in being able to mine alternate lurker-contributor behaviors over different layer networks.
Low-Voltage Super class AB CMOS OTA cells with very high slew rate and power efficiency
(2005)

A simple technique to achieve low-voltage power-efficient class AB operational transconductance amplifiers (OTAs) is presented. It is based on the combination of class AB differential input stages and local common-mode feedback (LCMFB) which provides additional dynamic current boosting, increased gain-bandwidth product (GBW), and near-optimal current efficiency. LCMFB is applied to various class AB differential input stages, leading to different class AB OTA topologies. Three OTA realizations based on this technique have been fabricated in a 0.5-/spl mu/m CMOS technology. For an 80-pF load they show enhancement factors of slew rate and GBW of up to 280 and 3.6, respectively, compared to a conventional class A OTA with the same 10-/spl mu/A quiescent currents and /spl plusmn/1-V supply voltages. In addition, the overhead in terms of common-mode input range, output swing, silicon area, noise, and static power consumption, is minimal.
Human gut microbiota in obesity and after gastric bypass.
(2009)

Recent evidence suggests that the microbial community in the human intestine may play an important role in the pathogenesis of obesity. We examined 184,094 sequences of microbial 16S rRNA genes from PCR amplicons by using the 454 pyrosequencing technology to compare the microbial community structures of 9 individuals, 3 in each of the categories of normal weight, morbidly obese, and post-gastric-bypass surgery. Phylogenetic analysis demonstrated that although the Bacteria in the human intestinal community were highly diverse, they fell mainly into 6 bacterial divisions that had distinct differences in the 3 study groups. Specifically, Firmicutes were dominant in normal-weight and obese individuals but significantly decreased in post-gastric-bypass individuals, who had a proportional increase of Gammaproteobacteria. Numbers of the H(2)-producing Prevotellaceae were highly enriched in the obese individuals. Unlike the highly diverse Bacteria, the Archaea comprised mainly members of the order Methanobacteriales, which are H(2)-oxidizing methanogens. Using real-time PCR, we detected significantly higher numbers of H(2)-utilizing methanogenic Archaea in obese individuals than in normal-weight or post-gastric-bypass individuals. The coexistence of H(2)-producing bacteria with relatively high numbers of H(2)-utilizing methanogenic Archaea in the gastrointestinal tract of obese individuals leads to the hypothesis that interspecies H(2) transfer between bacterial and archaeal species is an important mechanism for increasing energy uptake by the human large intestine in obese persons. The large bacterial population shift seen in the post-gastric-bypass individuals may reflect the double impact of the gut alteration caused by the surgical procedure and the consequent changes in food ingestion and digestion.
Sizing equation and Finite Element Analysis optimum design of axial-flux permanent-magnet motor for electric vehicle direct drive
(2012)

This paper presents the design process of a slotted TORUS axial-flux permanent-magnet motor suitable for direct drive of an electric vehicle through sizing equation and Finite Element Analysis. AFPM motor is a high torque density motor which can be easily and compactly mounted into the automobile's wheel fitting the rim perfectly. A double-sided slotted AFPM motor with 6 rotor poles for high torque density and stable rotation is the preliminary design. In order to determine the design requirements, a simple vehicle dynamic model evaluating the vehicle performance considering an automobile's typical-trip cursing scenario is considered. Axial-flux permanent-magnet machine's fundamental theory and sizing equation are applied to obtain the initial design parameters of the motor with the highest possible torque. The FEA of designed motor was conducted via commercial Vector Field Opera-3D 14.0 software for evaluation and accuracy enhancement of the design parameters. FEA simulation results are compared with those results obtained from sizing equation showing a good agreement of flux density values in various parts of the designed motor at no-load condition. The motor meets all the requirements and limitations of the electric vehicle fitting the shape and the size of a classical rim of the vehicle wheel.
Generic and unified model of Switched Capacitor Converters
(2009)

A generic modeling methodology that analyzes the losses in Switched Capacitors Converters (SCC) was developed and verified by simulation and experiments. The proposed analytical approach is unified, covering both hard and soft switched SCC topologies. The major advantage of the proposed model is that it expresses the losses as a function of the currents passing through each flying capacitor. Since these currents are linearly proportional to the output current, the model is also applicable to SCC with multiple capacitors. The proposed model provides an insight into the expected losses in SCC and the effects of their operational conditions such as duty cycle. As such, the model can help in the optimization of SCC systems and their control to achieve desired regulations.